论文标题
更快的本地化：在大规模环境中的高效且基于精确的LiDAR机器人本地化
Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
论文作者
论文摘要
本文提出了一种在大规模环境中全球移动机器人的全球定位方法的新方法。我们的方法利用基于学习的本地化和基于过滤的本地化，通过播种蒙特卡洛定位（MCL）具有深度学习的分布来有效，精确地定位机器人。特别是，快速定位系统通过深稳态模型（使用深核的高斯过程回归）迅速估算了6-DOF姿势，然后根据几何形状对准，精确的递归估计器优化了估计的机器人姿势。更重要的是，可以通过重要性采样自然整合了高斯方法（即深概率定位）和非高斯方法（即MCL）。因此，这两个系统可以无缝地集成并相互受益。为了验证所提出的框架，我们通过3D激光雷达传感器在大规模定位中提供了一个案例研究。我们在密歇根州NCLT长期数据集上进行的实验表明，所提出的方法能够将机器人平均在1.94 s（中位数为0.8 s）中，精度为0.75〜m，在约0.5 km2的大尺度环境中。
This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deep-learned distribution. In particular, a fast localisation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with a deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantly, the Gaussian method (i.e. deep probabilistic localisation) and non-Gaussian method (i.e. MCL) can be integrated naturally via importance sampling. Consequently, the two systems can be integrated seamlessly and mutually benefit from each other. To verify the proposed framework, we provide a case study in large-scale localisation with a 3D lidar sensor. Our experiments on the Michigan NCLT long-term dataset show that the proposed method is able to localise the robot in 1.94 s on average (median of 0.8 s) with precision 0.75~m in a large-scale environment of approximately 0.5 km2.
